Solving Regularized Least Squares with Qualitatively Controlled Adaptive Cross-Approximated Matrices

• Hamada Shoji

  Kyoto University

• Tatematsu Akiyoshi

  Central Research Institute of Electric Power Industry

Bibliographic Information

Other Title

• 近似品質管理付きＡＣＡによる正則化最小二乗解の高速計算法
• キンジ ヒンシツ カンリ ツキ ACA ニ ヨル セイソクカ サイショウ 2ジョウカイ ノ コウソク ケイサンホウ

Abstract

The adaptive cross-approximation (ACA) technique is applied to accelerating an inverse-problem solver that estimates charge distribution on a dielectric spacer. The ACA generates an approximated system-matrix that enables us to carry out high-speed inverse calculation. We designed an approximation procedure based on ACA with some additional concepts, that is, (a) partitioning of matrix based on algebraic information, (b) approximation quality control based on matrix-norms, and so on. The tested solver (LSQR for regularized least squares) with ACA demonstrates about 10 times faster performance than that by without ACA.

Journal

• IEEJ Transactions on Fundamentals and Materials

  IEEJ Transactions on Fundamentals and Materials 125 (5), 419-426, 2005

  The Institute of Electrical Engineers of Japan

Keywords

• approximated matrix
• least squares
• regularization
• conjugate gradient method
• charge measurement

Details

• CRID: 1390282679571092352
• NII Article ID: 10015518603
• NII Book ID: AN10136312
• DOI: 10.1541/ieejfms.125.419
• ISSN: 13475533
• ISSN: 03854205
• NDL BIB ID: 7342945
• Web Site: https://ndlsearch.ndl.go.jp/books/R000000004-I7342945
• Web Site: http://www.jstage.jst.go.jp/article/ieejfms/125/5/125_5_419/_pdf
• Text Lang: ja
• Data Source:
  • JaLC
  • NDL
  • Crossref
  • CiNii Articles
  • KAKEN
• Abstract License Flag: Disallowed